The article explores the intersection of natural language processing (NLP) and privacy-preserving techniques, focusing on the development of datasets and tools to anonymize sensitive information. It emphasizes the significance of the Text Anonymization Benchmark (TAB), which contains court cases to assess effective anonymization. Furthermore, it discusses various approaches, including differential privacy and entity recognition, which are crucial in mitigating privacy risks. The authors aim to address the need for more refined privacy-oriented methods to ensure user data protection in NLP applications, offering insights into ongoing challenges and future directions in this field.
The Text Anonymization Benchmark (TAB) corpus consists of 1268 manually annotated European Court of Human Rights court cases that protect the identities of individuals, demonstrating the importance of anonymization.
Our evaluation of privacy risk indicators identified key methods such as differential privacy and entity recognition as essential frameworks for safeguarding personal information in natural language processing.
#natural-language-processing #privacy-preservation #data-anonymization #entity-recognition #differential-privacy
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